We treat the single-camera captures as the training sequence in our multi-camera capture setup.
In effect, the single-camera capture setup will not have validation data for novel-view synthesis evaluation.
We preprocess the depth data for the training sequence by applying a Sobel filter [1]} to filter out inaccurate depth values around object edges.
In Figure REF , we visualize our depth data before and after filtering.
We find that NeRF is particularly sensitive to depth noise and this filtering step is necessary.
Finally, we manually annotate keypoints for correspondence evaluation.
For sequences of humans and quadrupeds (dogs or cats), we annotate keypoints based on the skeleton defined in the COCO challenge [2]} and StanfordExtra [3]}.
For sequences that focus on more general objects (e.g., our Block and Teddy sequences), we manually identify and annotate 5 to 15 trackable keypoints across frames.
We visualize keypoint annotations (with skeleton if available) for both our proposed iPhone dataset and the Nerfies-HyperNeRF dataset in Figure REF .
